Today’s state-of-the-art robots are capable of sub-millimeter movement accuracy when performing highly repeatable tasks. They perform extremely well in highly structured environments, where objects are in well-known, predictable locations. However, robots are not known for “thinking on the fly” to cope with unexpected events or changing situations. They are best when they can be programmed to perform a specific activity, which requires a specific set of motions, and that activity can be performed in the exact same way many hundreds or thousands of times. Not surprisingly, robots have been adopted much more in high volume, repeatable operations, such as painting and welding, rather than in smaller job shop type operations where only a handful of similar products are being made at a given time. Another way to describe this is that robots are not considered agile. Robot systems of the future need to perform their duties at least as well as human counterparts, be quickly re-tasked to other operations, and cope with a wide variety of unexpected environmental and operational changes in order for them to be useful to small manufacturers and to also allow larger manufacturers to offer more automated customization of high volume parts. Many computer-aided engineering approaches are beginning to show promise in helping robots become more agile, including techniques such as machine learning, cognitive modeling, artificial intelligence, knowledge representation and ontologies, dynamic and real-time planning, and real-time intelligent control. This special issue focuses on both theoretical and practical contributions of applying these techniques to improving the agility of robotic systems from an engineering and computer science perspective. The selection of papers for this special issue was extremely rigorous, with only six papers being published out of the many that were submitted. Each paper included in the special issue was reviewed by at least two rounds of reviews by three to eight reviewers. In addition, robot autonomy is multi-facetted, and we attempted to include a cross section of papers that addresses multiple relevant aspects. The algorithmic robot autonomy. Grasp Preimages Under Unknown Mass and Friction Distributions” an algorithm for computing robust grasp preimages, which the space of initial from which an